Mitigating Biases in Decision-Making Systems

Prediction-based decision-making systems are becoming increasingly prevalent in various domains. Previous studies have demonstrated that such systems are vulnerable to runaway feedback loops which exacerbate existing biases. The automated decisions have dynamic feedback effects on the system itself. In this talk we will show how existence of feedback loops in the machine learning-based decision-making pipeline can perpetuate and reinforce machine learning biases and propose strategies to counteract their undesired effects.